ALPR systems are employed in a variety of traffic surveillance applications, including toll monitoring, parking management, and the detection of different types of traffic violation. FIG. 1 illustrates a high-level block diagram of a prior art ALPR system 10. ALPR systems such as the example system 10 depicted in FIG. 1 typically has four stages 12, 14, 16, and 18. In the first stage 12, an image of a vehicle can be captured while the vehicle is moving (e.g., passing a toll booth). In the image acquisition stage 12, typically near infrared cameras are utilized to capture vehicle images both day and night time under low lighting conditions. In the second stage (i.e., plate localization) 14, the captured vehicle image is processed to localize the license plate region in the image. A number of different methods can be utilized for license plate localization.
After localizing the plate region in the image, the characters are segmented and extracted in the third stage 16 (i.e., character segmentation). In the final stage (i.e., character recognition) 18, the segmented character images can be recognized using an OCR (Optical Character Recognition) engine trained in an offline phase. A confidence evaluation operation 20 can be implemented after completion of the fourth stage 18. The OCR engine typically outputs a confidence score for each of the segmented character from which an overall confidence score is calculated for the entire plate. If the overall confidence score is higher than a pre-defined threshold as shown by decision operation 20, the recognized license plate number is directly passed to the rest of the processing pipeline without a human interruption. When the confidence score is less than the threshold, the license plate image first goes to a manual human review process 22 to avoid the serious public relations problem of issuing improper citations.
Though being a mature technology, the challenge with ALPR systems such as system 10 depicted in FIG. 1 is scalability and minimizing human intervention in the presence of challenging noise sources present in license plate images captured under realistic conditions (i.e., field deployed solutions). These include: heavy shadows, non-uniform illumination (from one vehicle to the next, daytime versus nighttime, etc.), challenging optical geometries (tilt, shear, or projective distortions), plate frames and/or stickers partially touching characters, partial occlusion of characters (e.g., trailer hitch ball), poor contrast, and general image noise (e.g., salt and pepper noise). For ALPR systems deployed in the USA, variation between states in character font, width, and spacing further add to the difficulty of proper character segmentation.